Factors Signaling a Movie's Success: Forecasting the Profitability of a Film During the Early Production Stage Based on its Internal Characteristics

The dynamic and lucrative nature of the creative industries, particularly the film sector, requires a comprehensive understanding of the determinants of financial success. This study employs a quantitative approach using the methods of data mining, semantic modeling, thematic distribution analysis, machine learning techniques, and econometrics analysis to develop and evaluate a predictive model for estimating a film's box office performance based on internal production signals. By analyzing historical data from "The Numbers" database, the paper aims to identify key factors influencing a film's financial viability during early production stages. As a result, the main signals having a significant impact on a film's financial success at the box office, encompassing emotional content, thematic coherence, and production-related estimators such as genre, source material, star power, and release timing, were determined, culminating in the validation of predictive models like random forest and neural networks. The findings of the study contribute to empowering stakeholders in making informed decisions, advancing the field of film production analytics and resulting in a more data-driven and financially sustainable film production ecosystem. Limitations regarding data quality and model transparency, including possible future expansion of the model to incorporate external factors, additional metrics and data sources, are acknowledged, as well as future research agendas outlined to address these concerns and achieve a more comprehensive understanding of financial performance prediction in the film industry.